On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification Using Text Line Based Features
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Analysis of Handwriting Individuality Using Word Features
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminatory Power of Handwritten Words for Writer Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A writer identification and verification system
Pattern Recognition Letters
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
A set of novel features for writer identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
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The paper presents a novel method for writer identification based on sparse representation of handwritten structural primitives, called graphemes or fraglets. The proposed method is different from the existing grapheme based methods as the earlier methods use vector quantization based coding (clustering method) to get a document descriptor, while the proposed method uses sparse coding for the same. Literature shows that the sparse coding outperforms vector quantization in many real life applications including face recognition. Sparse coding can achieve comparatively much lower reconstruction error. Secondly, the sparsity allows representation to be specialized and can capture a writer specific features more accurately. Graphemes (fraglets) extracted from a document are represented in terms of Fourier and wavelet descriptors because the fraglet contour may be well described by its global as well as local characteristics. Wavelet descriptors also give a multi-resolution representation of the shape. Results have shown that even with a smaller codebook (than the earlier reported systems), the proposed method achieves better performance.